雪氷チーム

Snow and Ice Research Team

車載カメラの画像解析による視程障害検知技術に関する研究
Visibility Hindrance Sensing Technology Based on Analysis of Images Taken by a Vehicle-mounted Camera
暴風雪被害の防止・軽減のため、対策の必要な箇所への吹雪対策施設の導入が進められていますが、適切な要対策箇所を抽出するには視程障害移動観測車で吹雪発生時に繰り返し観測する必要があり、データ取得のために時間とコストが必要になります。
そこで本研究は、視程障害移動観測車に搭載している車載カメラ映像と視程計のデータを使用し、ＡＩ（人工知能）による画像解析により吹雪の発生有無を検知する技術開発を進めています。

ビデオカメラVideo camera

To prevent and mitigate snowstorm damage, the introduction of blowing snow countermeasures has been promoted. In
determining the precise locations for countermeasures, it is necessary to make repeated observations using a visibility
observation vehicle during times of blowing snow. Time and finances are required for data acquisition.
For this study, we have been promoting the development of a technology that senses the presence or absence ofblowing snow by using AI that analyzes images. This technology utilizes the images taken by a video camera mounted
on a visibility observation vehicle and also the data collected by a visibility meter.

視程計
Visibility meter▲視程障害移動観測車
Visibility observation vehicle

深層学習による吹雪画像検知実験の概要

Overview of the experiment for determining blowing snow from images based on deep learning
The data for deep learning and the data for verification were still images extracted from digital video
学習用及び検証用のデータは、視程障害移動観測車に装着されているデジタ
ルビデオカメラの動画から静止画を切り出して使用し、切り出した画像の視程は、 images taken by a digital video camera mounted on the visibility observation vehicle. The visibility
distance for each still image was the average visibility distance of those from the time when the still
静止画を切り出した時刻から9秒後までの視程の平均値としています。
image was taken to seconds later.
学習用及び検証用データは、切り出された画像データと視程の値が１組にな
For deep learning and verification, we created data consisting of several hundreds of pairs of still
images and their visibility values. A determination model for deep learning was created using theseっており、それぞれ数百枚ずつ作成します。
これらのデータを用いて深層学習にdata. To improve the accuracy of the system, the parameters will be continuously improved based on
よる判別モデルの作成を行い、検証と評価によるパラメータの改善やデータのverification and on the selection and evaluation of data.
選別などを重ねながら精度の向上を図ります。
Blowing snow was determined to occur when the visibility distance was 200m or less.
When the visibility distance exceeded 200m, the condition was determined to be the absence of
吹雪の検知は視程200mで吹雪発生、視程200m以上を吹雪未発生として
blowing snow.
います。

実験の手順

作成した学習用及び検証用データ

切り出し時刻The created data for deep learningand those for verification

Time when the image was extracted

Procedure of the experiment
Step

1

Step

2

Step

3

学習用データ作成

Step

Create data forlearning.

1’

視検証用データ作成

Create data forverification.

Step

7秒

6秒

5秒

4秒

3秒

2秒

画像切り出し1秒

9 sec. 8 sec. 7 sec. 6 sec. 5 sec. 4 sec. 3 sec. 2 sec. 1 sec.

程: ●(m)Extraction of image

深層学習の実施Execute deep learning.

●m●m●m●m●m視程●m●m●mVisibility distance: ● (m)- The average value for the visibility
distances measured from the time ofextraction of the image to 9 seconds later

Create a blowing snow
determination model.

吹雪判別モデルによる判定

Use the model to make determinations.

吹雪判別モデルの評価●mVisibility distance

吹雪判別モデルの作成

Input the data for verification.

48秒※切り出しから9秒後までの平均値

検証用データ投入

Step9秒動画から静止画を切り出し、切り出した時刻から9秒後までの視程の平均をその画像の視程とする。
※視程計の計測周期は0.1秒間に1回（車両から100ｍ前方までの視程値が9秒後までに相当する）
A still image is extracted from digital video. The visibility distance of this image was set as the average of the visibility distances
measured from the time of image extraction to 9 seconds later.

5 Evaluate the blowing snow determination model.

Note: A measurement was taken by the visibility meter every 0.1 second (The visibility value for the 100m ahead of the vehicle
corresponds to the visibility 9 seconds later).

吹雪画像検知実験の結果の一例

Example of the experiment for determining blowing snow from images
作成した吹雪画像判別モデルは、吹雪発生、 The F-scores for blowing snow and no blowing snow were
吹雪未発生ともF-scoreが80%以上となり、 80% or higher for the model created for determining blowing
snow images. Based on this result, it was found possible to
画像から吹雪検知の可能性を見いだすことがdetect blowing snow using images.
できました。
In addition to sensing blowing snow, we will categorize the
今後は吹雪の検知のみならず、視程を数段visibility according to several levels, create a model for
階にカテゴリ分けし、判別するモデルを作成し、 determining the degree of visibility, and continue to verify themodel.
検証していきます。

カテゴリ
Category
吹雪発生

Blowing snow

適合率

再現率

F値

正解率

Precision

RecallF-scoreAccuracy

97.9%

79.4%

87.7%
86.0%

吹雪未発生
No blowing snow

71.0%

96.7%

▲吹雪画像判別モデルの精度

81.9%

Accuracy of the model for determining blowing snow images

安全・快適

北の道Safe, pleasant driving on Northern Roads

国立研究開発法人土木研究所 寒地土木研究所

National Research and Development Agency Public Works Research Institute Civil Engineering Research Institute for Cold Region